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arXiv · 2511.22481

OmniInfer: System-Wide Acceleration Techniques for Optimizing LLM Serving Throughput and Latency

Jun Wang·Yunxiang Yao·Wenwei Kuang·Runze Mao·Zhenhao Sun·Zhuang Tao·Ziyang Zhang·Dengyu Li·Jiajun Chen·Zhili Wang·Kai Cui·Congzhi Cai·Longwen Lan·Ken Zhang
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Abstract

Large Language Models drive a wide range of modern AI applications but impose substantial challenges on large-scale serving systems due to intensive computation, strict latency constraints, and throughput bottlenecks. We introduce OmniInfer, a unified system-level acceleration framework designed to maximize end-to-end serving efficiency through fine-grained optimization of expert placement, cache compression, and scheduling. OmniInfer integrates three complementary components: OmniPlacement for load-aware Mixture-of-Experts scheduling, OmniAttn for sparse attention acceleration, and OmniProxy for disaggregation-aware request scheduling. Built atop vLLM, OmniInfer delivers system-wide performance gains through adaptive resource disaggregation, efficient sparsity exploitation, and global coordination across prefill and decode phases. Evaluated on DeepSeek-R1 within a 10-node Ascend 910C cluster, OmniInfer achieves 616 QPM, where the unified framework reduces TPOT by 36\%, and the superimposition of OmniProxy further slashes TTFT by 38\%. The project is open-sourced at [this https URL](https://gitee.com/omniai/omniinfer).

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Jun Wang, Yunxiang Yao, Wenwei Kuang, Runze Mao, Zhenhao Sun, Zhuang Tao, Ziyang Zhang, Dengyu Li, Jiajun Chen, Zhili Wang, Kai Cui, Congzhi Cai, Longwen Lan, Ken Zhang. 2025-11-27. OmniInfer: System-Wide Acceleration Techniques for Optimizing LLM Serving Throughput and Latency. https://arxiv.org/abs/2511.22481

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